• Journal of Animal Science and Technology
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• v.63 no.6
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• pp.1464-1467
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• 2021

Bacillus coagulans CACC 834 was isolated from canine feces, and its potential probiotic properties were characterized by functional genome analysis. Whole-genome sequencing of B. coagulans CACC 834 was performed using the PacBio RSII platforms. The complete genome assembly consisted of one circular chromosome (3.1 Mb) with guanine (G) + cytosine (C) content of 47.1%. Annotation revealed 3,181 protein-coding sequences (CDSs), 30 rRNAs, and 83 tRNAs. Gene associated 11% of the genes were involved in replication, recombination, and repair. We also annotated various stress-related, acid resistance, bile salt resistance and adhesion-related domains in this strain, which likely provide support in exerting probiotic action by survival under gastrointestinal tract. These results add to our comprehensive understanding of B. coagulans and suggest potential mammal-related industrial applications.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.123-141
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• 2019

The goal of this study is to review the major research trend on the convergence studies of AI and healthcare technologies. For the study, 15,260 English articles on AI and healthcare related topics were collected from Scopus for 55 years from 1963, and text mining techniques were conducted. As a result, seven key research topics were defined : "AI for Clinical Decision Support System (CDSS)", "AI for Medical Image", "Internet of Healthcare Things (IoHT)", "Big Data Analytics in Healthcare", "Medical Robotics", "Blockchain in Healthcare", and "Evidence Based Medicine (EBM)". The result of this study can be utilized to set up and develop the appropriate healthcare R&D strategies for the researchers and government. In this study, text mining techniques such as Text Analysis, Frequency Analysis, Topic Modeling on LDA (Latent Dirichlet Allocation), Word Cloud, and Ego Network Analysis were conducted.

• Journal of Animal Science and Technology
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• v.63 no.1
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• pp.191-193
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• 2021

Lactococcus lactis is a fermentative lactic acid bacterium that is used extensively in food fermentations. The L. lactis strain K_LL005 was isolated from the grasshopper (Oxya chinensis sinuosa) gut in Korea. In this study, we reported the complete genome sequence of Lactococcus lactis K_LL005. The final complete genome assembly consist of one circular chromosome (2,375,093 bp) with an overall guanine + cytosine (G + C) content of 35.0%. Annotation results revealed 2,281 protein-coding sequences (CDSs), 19 rRNAs, and 68 tRNA genes. Lactococcus lactis K_LL005 has a gene encoding xylose metabolism such as xylR, xylA, and xylB (xylRAB).

• Journal of Animal Science and Technology
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• v.49 no.5
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• pp.549-558
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• 2007

C4B and BAT2, assigned to the SLA class III region, were recently reported on relation with human diseases. The primers for RT-PCR and RACE-PCR for CDS analysis of these genes of pig were designed by aligning the CDSs of humans and mice from GenBank. After we amplified and sequenced with these primers and cDNAs, the full-length CDSs of pig were determined. The CDS lengths of C4B and BAT2 were shown as 5226 bp and 6501 bp. In addition, the identities of nucleotide sequences with human and mouse were 76% to 87%, and the identities of amino acids were 72% to 90%. After we carried out the alignment with determined CDSs in this study and pig genomic sequences from GenBank, the primers for cSNP detection in genome were designed in intron regions that flanked one or more exons. Then, we amplified and directly sequenced with genomic DNAs of six pig breeds. Four cSNPs from C4B and three 3 cSNPs from BAT2 were identified. In addition, amino acid substitution occurred in six cSNP positions except for C4248T of C4B. By the Multiplex-ARMS method, we genotyped seven cSNPs with DNA samples used for direct sequencing. We verified that this result was the same as that analyzed using direct sequencing. To demonstrate recrudescence, we performed both direct sequencing and Multiplex-ARMS on two randomly selected DNA samples. The genotype of each sample showed the same result from both methods. Therefore, seven cSNPs were identified from C4B and BAT2 and could be used as the basic data for haplotype analysis of SLA class III region. Moreover, the Multiplex-ARMS method should be powerful for genotyping of genes assigned to the whole SLA region for the xenograft study.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.10
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• pp.239-246
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• 2020

In the medical field where artificial intelligence technology is introduced, research related to clinical decision support system(CDSS) in relation to diagnosis and prediction is actively being conducted. In particular, medical imaging-based disease diagnosis area applied AI technologies at various products. However, medical imaging data consists of inconsistent data, and it is a reality that it takes considerable time to prepare and use it for research. This paper describes a one-stop AI learning platform for converting to medical image standard R_CDM(Radiology Common Data Model) and supporting AI algorithm development research based on the dataset. To this, the focus is on linking with the existing CDM(common data model) and model the system, including the schema of the medical imaging standard model and report information for multi-center research based on DICOM(Digital Imaging and Communications in Medicine) tag information. And also, we show the execution results based on generated datasets through the AI learning platform. As a proposed platform, it is expected to be used for various image-based artificial intelligence researches.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.21-22
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• 2000

Expressed sequence tags (EFTs) are the partial segments of cDNA produced from 5 or 3 single-pass sequencing of cDNA clones, error-prone and generated in highly redundant sets. Advancement and expansion of Genomics made biologists to generate huge amount of ESTs from variety of organisms-human, microorganisms as well as plants, and the cumulated number of ESTs is over 5.3 million, As the EST data being accumulate more rapidly, it becomes bigger that the needs of the EST analysis tools for extraction of biological meaning from EST data. Among the several needs of EST analyses, the extraction of protein sequence or functional motifs from ESTs are important for the identification of their function in vivo. To accomplish that purpose the precise and accurate identification of the region where the coding sequences (CDSs) is a crucial problem to solve primarily, and it will be helpful to extract and detect of genuine CD5s and protein motifs from EST collections. Although several public tools are available for EST analysis, there is not any one to accomplish the object. Furthermore, they are not targeted to the plant ESTs but human or microorganism. Thus, to correspond the urgent needs of collaborators deals with plant ESTs and to establish the analysis system to be used as general-purpose public software we constructed the pipelined-EST analysis system by integration of public software components. The software we used are as follows - Phred/Cross-match for the quality control and vector screening, NCBI Blast for the similarity searching, ICATools for the EST clustering, Phrap for EST contig assembly, and BLOCKS/Prosite for protein motif searching. The sample data set used for the construction and verification of this system was 1,386 ESTs from human intrathymic T-cells that verified using UniGene and Nr database of NCBI. The approach for the extraction of CDSs from sample data set was carried out by comparison between sample data and protein sequences/motif database, determining matched protein sequences/motifs that agree with our defined parameters, and extracting the regions that shows similarities. In recent future, in addition to these components, it is supposed to be also integrated into our system and served that the software for the peptide mass spectrometry fingerprint analysis, one of the proteomics fields. This pipelined-EST analysis system will extend our knowledge on the plant ESTs and proteins by identification of unknown-genes.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.722-729
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• 2010

Prediction problems are widely used in medical domains. For example, computer aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs with nonlinear kernels like RBF kernels, have shown superior accuracy in prediction problems. However, they are not preferred by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized Radial Basis Function (LRBF) was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models, VRIFA visualizes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The area under the ROC curve (AUC) can be used to evaluate the prediction result when the data set is highly imbalanced. The tool can be used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.19-24
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• 2019

Lung cancer is a chronic disease which ranks fourth in cancer incidence with 11 percent of the total cancer incidence in Korea. To deal with such issues, there is an active study on the usefulness and utilization of the Clinical Decision Support System (CDSS) which utilizes machine learning. Thus, this study reviews existing studies on artificial intelligence technology that can be used in determining the lung cancer, and conducted a study on the applicability of machine learning in determination of the lung cancer by comparison and analysis using Azure ML provided by Microsoft. The results of this study show different predictions yielded by three algorithms: Support Vector Machine (SVM), Two-Class Support Decision Jungle and Multiclass Decision Jungle. This study has its limitations in the size of the Big data used in Machine Learning. Although the data provided by Kaggle is the most suitable one for this study, it is assumed that there is a limit in learning the data fully due to the lack of absolute figures. Therefore, it is claimed that if the agency's cooperation in the subsequent research is used to compare and analyze various kinds of algorithms other than those used in this study, a more accurate screening machine for lung cancer could be created.

• Journal of Haehwa Medicine
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• v.23 no.1
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• pp.13-20
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• 2014

Objectives : Clinical data in traditional medicine, such as Korean medicine, traditional Chinese medicine have a long history of accumulating evidence and these rich data are recorded in classic literature. We have conducted a study of developing an algorithm that support clinical diagnosis with composing both users knowledge and data obtained from literature. In order to define necessary information and required steps in diagnosis procedure, we have established a clinical diagnostic procedure including a step of collecting patients symptoms, a step of determining candidates, a step of diagnostic decisions, a step of deciding of treatment and a step of adjusting medicinal treatment. Methods : Our study have been based on the following premises. 1. Using data obtained from literature contributes to accurate diagnosis 2. Displaying the data before users request contributes to accurate conclusion. Displaying before users request enable users to recognize their overlooking a fact on purpose or not. 3. Checking symptoms that are commonly accompanied with a group of diseases that accompany symptoms appealed by a patient contributes to accurate conclusion. These symptoms are worthy of checking. 4. Comparing more than two candidates contributes to accurate conclusion. Users can compare their accompanied symptoms with patients symptoms and this helps users to make a decision. Results : Based on the above premises, we have developed an literature based algorithm to provide various functions, such as recommending symptoms to check, comparing groups of symptoms, differential diagnosis, recommending medicinal materials to prescribe, and more. Conclusions : By the results of simulation with virtual diagnostic scenario, we concluded this algorithm is useful helping clinician in diagnosis procedure.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.5
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• pp.942-949
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• 2009

We in this paper propose the method for diagnosis patients through the reasoning based on the diagnosis ontology in oriental medicine. In prior studies, it is simply diagnosed with the information of main symptoms, optional symptoms, and tongue / pulse. In addition, ontology itself has subjective opinions of oriental medical doctors for patients in form of axioms. There is a problem in latter case that it is difficult for other oriental medical doctors to change knowledge within the ontology. In order to solve these problems, we have constructed the diagnosis ontology and the reasoning algorithm as followings: First, in order to raise the diagnosis accuracy, we constructed the diagnosis ontology with pattern identifications, main symptoms, optional symptoms, and tongue / pulse. We also utilize the diagnosis points described in the pathology textbook, which has been studied in all of domestic oriental medical colleges. This information is represented as OWL instances in ontology, not OWL axioms so that it can be easily updated. Second, we suggest the algorithms for diagnosis reasoning and learning method based on the ontology. We have implemented the reasoning and learning system according to the diagnosis algorithm. In future study, we will construct the diagnosis ontology with all of pattern identifications and symptoms within the pathology textbook.